Semantic parsing is the task of mapping a natural language input (e.g., a natural language utterance or other input) to a logical form (such as Prolog or lambda calculus), which is easier for a computer to understand. Usually, the logical form can be executed directly through database query. Semantic parsing needs application or domain specific training data, so the conventional approach is to manufacture training data for each combination of language and application domain. Semantic parsing has mostly been developed for only certain languages (e.g., so-called high resource languages) because it is costly and time consuming to build.
Although techniques have been developed for transfer learning across domains, conventional systems have not used such transfer learning for languages for which semantic parsing has not been developed. For example, various transfer learning approaches may not work well for cross-lingual transfer learning (i.e., transfer from one language to another), because there may not be many shared features between the two languages.
These and other problems exist for developing semantic parsers for multiple languages, including for transfer learning across different languages.